Deep learning with PyTorch
9:00am - 5:00pm
What you'll learn, and how you can apply it
- Understand PyTorch's tensors and automatic differentiation package
- Examine different deep learning model architectures
- Learn to build and train deep neural networks in PyTorch
Who is this presentation for?
- You're a developer or analyst with some machine learning and Python experience.
Level
Prerequisites:
- A basic understanding of Python, matrices and linear algebra, modeling and machine learning, and neural networks
Outline
Day 1
- PyTorch tensors
- Automatic differentiation package
- Neural networks
- Multilayer perceptrons
Day 2
- Network architectures
- Convolutional neural network
- Autoencoders
About your instructor
Richard Ott obtained his PhD in particle physics from the Massachusetts Institute of Technology, followed by postdoctoral research at the University of California, Davis. He then decided to work in industry, taking a role as a data scientist and software engineer at Verizon for two years. When the opportunity to combine his interest in data with his love of teaching arose at The Data Incubator, he joined and has been teaching there ever since.
Conference registration
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